Stimulus-independent and stimulus-dependent neural networks underpin placebo analgesia responsiveness in humans

The neural circuits that regulate placebo analgesia responsivity are unknown, although engagement of brainstem pain modulatory regions is likely critical. Here we show in 47 participants that differences are present in neural circuit connectivity’s in placebo responders versus non-responders. We distinguish stimulus-independent and stimulus-dependent neural networks that display altered connections between the hypothalamus, anterior cingulate cortex and midbrain periaqueductal gray matter. This dual regulatory system underpins an individual’s ability to mount placebo analgesia.

Participants were recruited through experimental notices distributed throughout the university of Melbourne, Australia. Due to the deceptive nature of the experiment (placebo analgesia), they were informed this study was investigating "human brain imaging of pain", looking at neural responses after application of a neutral control and active analgesic cream.
All experimental procedures were approved by the University of Sydney Human Research Ethics Committee and were consistent with the Declaration of Helsinki.
This study involved conditioning healthy human participants to believe a sham placebo cream labelled and described as "lidocaine", a potent analgesic, was working to modulate their pain relative to a control vaseline cream. Using human brain imaging, connectivity, and dynamic causal modelling, we hypothesise the existence of two seperate top-down networks which coordinate the output in descending modulatory pathways of the brainstem responsible for the manifestation of Placebo Analgesia.
The research sample was largely formed by University students and researchers in adjacent fields at the University of Melbourne.
An apriori power analysis was conducted using Eippert et al. (2009) findings of cortico-brainstem communication. This revealed a total sample size of at least 40 would be necessary to detect similar effect sizes with 95% power (d = 0.31, ! = 0.05, power = 0.95). We elected to sample a larger number of participants due to signal artefact which can arise during human brain imaging, excluding some functional datasets. We observed no significant signal or structural artefact in any of our 47 participants, and as such included all functional data to meet the criteria of our power analysis and bolster the strength of any potential findings. Human brain imaging was recorded using the 7T MRI described above. Noxious stmuli were applied using a 3x3cm Peltier element thermode (Medoc). Pain rating data was recorded throughout the course of the study using a Visual Analogue Scale, which participants used to dynamically report their pain both outside (conditioning and reinforcement) and inside (test) the scanner.
Data collection occurred consistently throughout the years 2021-2022. Due to the constraints induced by COVID-19, we were unable to collect any data throughout lockdown periods in Australia.

No data was excluded from this study
No participant drop out occurred throughout this study.
Participants were allocated to a placebo "responder" or "nonresponder" group using the 2 Standard Deviation band method for determining significant deviations in typical pain processing that occur during placebo analgesia. This method involves calculating the standard deviation of pain responses in a single participant across a multi-trial acute pain design (stimulation of the control site), multiplying this number by two, and then calculating the average pain response in a single participant across a multi-trial modulated pain site (stimulation of the placebo "lidocaine" site). If this average is greater than two standard deviations lower than their pain responses on the control site, the participant is considered a responder. If they do not meet this criteria, they are considered a nonresponder.

March 2021
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response. Ongoing pain responses were recorded throughout all experimental phases using a computerized Visual Analogue Scale (VAS). That is, this rating system dynamically recorded participant pain responses at all times on a time scale consistent with volumes recorded during fMRI collection.

Structural (T1-weighted), and functional 7 Tesla
A T1-weighted anatomical image set covering the whole brain was collected (repetition time=5000 ms, echo time=3.1ms, raw voxel size=0.73x0.73x0.73mm, 224 sagittal slices, scan time=7mins). The two fMRI acquisitions each consisted of a series of 134 gradient echo echo-planar measurements using blood oxygen level dependant (BOLD) contrast covering the entire brain. Images were acquired in an interleaved collection pattern with a multi-band factor of four and an acceleration factor of three (repetition time=2500ms, echo time=26ms; raw voxel size=1.0x1.0x1.2mm, 124 axial slices, scan time=5:35mins).
Whole brain coverage was recorded in both the T1-weighted and Functional brain scans Statistical Parametric Mapping Version 12 (SPM12). The first five volumes of each scan were removed from the model due to excessive signal saturation from the scanner. The remaining 129 functional images were slice-time and motion corrected and the resulting 6 directional movement parameters were inspected to ensure that all fMRI scans had no greater than 1mm of linear movement or 0.5 degrees of rotation movement in any direction. Images were spatially smoothed using a 6mm Fullwidth-at-half-maximum gaussian kernel.
Each individual's fMRI image sets were then coregistered to their own T1-weighted anatomical, the T1 was then spatially normalized to the DARTEL template in Montreal Neurological Institute (MNI) space and the parameters applied to the fMRI image sets.

Dartel template in MNI space
Images were then linearly detrended to remove global signal changes, physiological noise relating to cardiac and respiratory frequency was removed using the DRIFTER toolbox (Särkkä, S. et al. NeuroImage 60, 1517-1527, (2012), and the 6parameter movement related signal changes were modelled and removed using a linear modelling of realignment parameters (LMRP) procedure.
The first five volumes of each scan were removed from the model due to excessive signal saturation from the scanner.
Random-effects analyses, paired statistical analyses, Dynamic Causal Modelling settings: slice timing = 1.25s (modelled to the centre slice of acquisition), echo time = 0.026s, bilinear modulatory effects, one state per region, stochastic effects off, centred inputs on, and a timeseries fit. Mediation settings: FDR-corrected p<0.05, bootstrapped to 10,000 samples.